Overview

Dataset statistics

Number of variables68
Number of observations14829
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 MiB
Average record size in memory537.0 B

Variable types

Numeric5
Boolean1
Categorical62

Alerts

display_1 has constant value "0"Constant
display_A has constant value "0"Constant
Unnamed: 0 is highly overall correlated with homeowner_Probable OwnerHigh correlation
pct_disc is highly overall correlated with pct_retail_discHigh correlation
pct_retail_disc is highly overall correlated with pct_discHigh correlation
marital_status_A is highly overall correlated with hhsize_1High correlation
homeowner_Probable Owner is highly overall correlated with Unnamed: 0High correlation
homeowner_Probable Renter is highly overall correlated with income_15-24KHigh correlation
hhcomp_1 Adult Kids is highly overall correlated with kid_category_None/UnknownHigh correlation
hhcomp_2 Adults Kids is highly overall correlated with kid_category_None/UnknownHigh correlation
hhcomp_2 Adults No Kids is highly overall correlated with hhsize_2High correlation
hhcomp_Single Female is highly overall correlated with hhsize_1High correlation
kid_category_1 is highly overall correlated with kid_category_None/Unknown and 1 other fieldsHigh correlation
kid_category_2 is highly overall correlated with hhsize_4High correlation
kid_category_3+ is highly overall correlated with hhsize_5+High correlation
kid_category_None/Unknown is highly overall correlated with hhcomp_1 Adult Kids and 3 other fieldsHigh correlation
age_35-44 is highly overall correlated with age_45-54High correlation
age_45-54 is highly overall correlated with age_35-44High correlation
age_55-64 is highly overall correlated with income_150-174KHigh correlation
income_15-24K is highly overall correlated with homeowner_Probable RenterHigh correlation
income_150-174K is highly overall correlated with age_55-64High correlation
hhsize_1 is highly overall correlated with marital_status_A and 2 other fieldsHigh correlation
hhsize_2 is highly overall correlated with hhcomp_2 Adults No Kids and 1 other fieldsHigh correlation
hhsize_3 is highly overall correlated with kid_category_1 and 1 other fieldsHigh correlation
hhsize_4 is highly overall correlated with kid_category_2High correlation
hhsize_5+ is highly overall correlated with kid_category_3+High correlation
campaign_13.0 is highly overall correlated with description_TypeAHigh correlation
campaign_15.0 is highly overall correlated with description_TypeCHigh correlation
campaign_24.0 is highly overall correlated with description_TypeBHigh correlation
description_TypeA is highly overall correlated with campaign_13.0High correlation
description_TypeB is highly overall correlated with campaign_24.0High correlation
description_TypeC is highly overall correlated with campaign_15.0High correlation
display_2 is highly imbalanced (99.9%)Imbalance
display_3 is highly imbalanced (98.2%)Imbalance
display_4 is highly imbalanced (99.8%)Imbalance
display_5 is highly imbalanced (96.0%)Imbalance
display_6 is highly imbalanced (99.8%)Imbalance
display_7 is highly imbalanced (99.6%)Imbalance
display_9 is highly imbalanced (96.6%)Imbalance
mailer_A is highly imbalanced (86.6%)Imbalance
mailer_C is highly imbalanced (99.8%)Imbalance
mailer_F is highly imbalanced (99.6%)Imbalance
mailer_H is highly imbalanced (95.6%)Imbalance
mailer_L is highly imbalanced (99.6%)Imbalance
homeowner_Probable Owner is highly imbalanced (76.0%)Imbalance
homeowner_Probable Renter is highly imbalanced (85.2%)Imbalance
homeowner_Renter is highly imbalanced (58.0%)Imbalance
hhcomp_1 Adult Kids is highly imbalanced (55.7%)Imbalance
hhcomp_Single Male is highly imbalanced (70.0%)Imbalance
kid_category_2 is highly imbalanced (62.4%)Imbalance
kid_category_3+ is highly imbalanced (70.2%)Imbalance
age_19-24 is highly imbalanced (72.0%)Imbalance
age_55-64 is highly imbalanced (66.8%)Imbalance
age_65+ is highly imbalanced (68.0%)Imbalance
income_100-124K is highly imbalanced (91.8%)Imbalance
income_125-149K is highly imbalanced (76.5%)Imbalance
income_15-24K is highly imbalanced (67.7%)Imbalance
income_150-174K is highly imbalanced (63.8%)Imbalance
income_175-199K is highly imbalanced (96.4%)Imbalance
income_200-249K is highly imbalanced (95.1%)Imbalance
income_250K+ is highly imbalanced (93.6%)Imbalance
income_Under 15K is highly imbalanced (69.4%)Imbalance
hhsize_4 is highly imbalanced (74.0%)Imbalance
hhsize_5+ is highly imbalanced (71.2%)Imbalance
campaign_8.0 is highly imbalanced (94.7%)Imbalance
campaign_13.0 is highly imbalanced (84.0%)Imbalance
campaign_15.0 is highly imbalanced (89.7%)Imbalance
campaign_18.0 is highly imbalanced (94.5%)Imbalance
campaign_24.0 is highly imbalanced (78.3%)Imbalance
campaign_26.0 is highly imbalanced (96.5%)Imbalance
campaign_30.0 is highly imbalanced (96.4%)Imbalance
description_TypeA is highly imbalanced (74.3%)Imbalance
description_TypeB is highly imbalanced (78.3%)Imbalance
description_TypeC is highly imbalanced (89.7%)Imbalance
Unnamed: 0 has unique valuesUnique
pct_disc has 6582 (44.4%) zerosZeros
pct_retail_disc has 6726 (45.4%) zerosZeros
pct_coupon_disc has 14386 (97.0%) zerosZeros

Reproduction

Analysis started2023-05-28 08:57:48.039512
Analysis finished2023-05-28 08:58:23.162552
Duration35.12 seconds
Software versionpandas-profiling v0.0.dev0
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct14829
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11194.865
Minimum25
Maximum22226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:58:23.302695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile1286.4
Q15333
median11262
Q317125
95-th percentile21112.6
Maximum22226
Range22201
Interquartile range (IQR)11792

Descriptive statistics

Standard deviation6516.8553
Coefficient of variation (CV)0.58212898
Kurtosis-1.2554805
Mean11194.865
Median Absolute Deviation (MAD)5896
Skewness-0.0051061725
Sum1.6600865 × 108
Variance42469403
MonotonicityStrictly increasing
2023-05-28T10:58:23.448572image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 1
 
< 0.1%
15381 1
 
< 0.1%
15383 1
 
< 0.1%
15384 1
 
< 0.1%
15385 1
 
< 0.1%
15386 1
 
< 0.1%
15387 1
 
< 0.1%
15388 1
 
< 0.1%
15389 1
 
< 0.1%
15390 1
 
< 0.1%
Other values (14819) 14819
99.9%
ValueCountFrequency (%)
25 1
< 0.1%
26 1
< 0.1%
27 1
< 0.1%
28 1
< 0.1%
29 1
< 0.1%
30 1
< 0.1%
31 1
< 0.1%
32 1
< 0.1%
33 1
< 0.1%
34 1
< 0.1%
ValueCountFrequency (%)
22226 1
< 0.1%
22225 1
< 0.1%
22224 1
< 0.1%
22223 1
< 0.1%
22222 1
< 0.1%
22221 1
< 0.1%
22220 1
< 0.1%
22219 1
< 0.1%
22218 1
< 0.1%
22217 1
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
False
10163 
True
4666 
ValueCountFrequency (%)
False 10163
68.5%
True 4666
31.5%
2023-05-28T10:58:23.585003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

shelf_price
Real number (ℝ)

Distinct278
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8103436
Minimum0.12
Maximum29.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:58:23.700207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.25
Q10.5
median0.69
Q33.59
95-th percentile11.99
Maximum29.99
Range29.87
Interquartile range (IQR)3.09

Descriptive statistics

Standard deviation4.3149551
Coefficient of variation (CV)1.5353835
Kurtosis9.2495178
Mean2.8103436
Median Absolute Deviation (MAD)0.4
Skewness2.7417866
Sum41674.585
Variance18.618838
MonotonicityNot monotonic
2023-05-28T10:58:23.827433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 2847
 
19.2%
0.25 972
 
6.6%
0.69 766
 
5.2%
0.79 370
 
2.5%
0.89 313
 
2.1%
2.99 282
 
1.9%
0.33 273
 
1.8%
0.59 264
 
1.8%
0.24 259
 
1.7%
0.39 251
 
1.7%
Other values (268) 8232
55.5%
ValueCountFrequency (%)
0.12 1
 
< 0.1%
0.19 1
 
< 0.1%
0.2 6
 
< 0.1%
0.2 11
 
0.1%
0.24 259
 
1.7%
0.25 972
6.6%
0.27 59
 
0.4%
0.28 45
 
0.3%
0.28 157
 
1.1%
0.29 64
 
0.4%
ValueCountFrequency (%)
29.99 36
0.2%
29.79 24
0.2%
28.99 1
 
< 0.1%
26.99 1
 
< 0.1%
25.99 6
 
< 0.1%
21.99 34
0.2%
20.99 6
 
< 0.1%
20.89 12
 
0.1%
20.69 3
 
< 0.1%
20.49 11
 
0.1%

pct_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct778
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1295983
Minimum0
Maximum0.98657718
Zeros6582
Zeros (%)44.4%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:58:23.979151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.090909091
Q30.24444444
95-th percentile0.37106918
Maximum0.98657718
Range0.98657718
Interquartile range (IQR)0.24444444

Descriptive statistics

Standard deviation0.14630139
Coefficient of variation (CV)1.1288835
Kurtosis0.68616695
Mean0.1295983
Median Absolute Deviation (MAD)0.090909091
Skewness0.9536825
Sum1921.8132
Variance0.021404096
MonotonicityNot monotonic
2023-05-28T10:58:24.115620image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6582
44.4%
0.2753623188 497
 
3.4%
0.34 402
 
2.7%
0.22 383
 
2.6%
0.358974359 237
 
1.6%
0.32 195
 
1.3%
0.3333333333 184
 
1.2%
0.1265822785 181
 
1.2%
0.4 145
 
1.0%
0.1304347826 132
 
0.9%
Other values (768) 5891
39.7%
ValueCountFrequency (%)
0 6582
44.4%
0.002222222222 1
 
< 0.1%
0.004282655246 1
 
< 0.1%
0.005 1
 
< 0.1%
0.005617977528 2
 
< 0.1%
0.006734006734 1
 
< 0.1%
0.007054673721 1
 
< 0.1%
0.008403361345 1
 
< 0.1%
0.008403361345 29
 
0.2%
0.008739076155 1
 
< 0.1%
ValueCountFrequency (%)
0.9865771812 1
 
< 0.1%
0.9485094851 1
 
< 0.1%
0.9090909091 3
< 0.1%
0.9036144578 1
 
< 0.1%
0.8866666667 1
 
< 0.1%
0.8823529412 1
 
< 0.1%
0.863574352 1
 
< 0.1%
0.8567335244 1
 
< 0.1%
0.8474576271 1
 
< 0.1%
0.8416886544 1
 
< 0.1%

pct_retail_disc
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct585
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12222027
Minimum-0
Maximum0.98657718
Zeros6726
Zeros (%)45.4%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:58:24.255201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q10
median0.076982294
Q30.23255814
95-th percentile0.35897436
Maximum0.98657718
Range0.98657718
Interquartile range (IQR)0.23255814

Descriptive statistics

Standard deviation0.13654426
Coefficient of variation (CV)1.1171982
Kurtosis-0.3534032
Mean0.12222027
Median Absolute Deviation (MAD)0.076982294
Skewness0.74341416
Sum1812.4043
Variance0.018644334
MonotonicityNot monotonic
2023-05-28T10:58:24.537655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 6726
45.4%
0.2753623188 529
 
3.6%
0.22 466
 
3.1%
0.34 402
 
2.7%
0.358974359 251
 
1.7%
0.32 195
 
1.3%
0.3333333333 183
 
1.2%
0.1265822785 183
 
1.2%
0.1304347826 179
 
1.2%
0.4 123
 
0.8%
Other values (575) 5592
37.7%
ValueCountFrequency (%)
-0 6726
45.4%
0.002222222222 1
 
< 0.1%
0.004282655246 1
 
< 0.1%
0.005 1
 
< 0.1%
0.005617977528 2
 
< 0.1%
0.006734006734 1
 
< 0.1%
0.007054673721 1
 
< 0.1%
0.008403361345 30
 
0.2%
0.008739076155 1
 
< 0.1%
0.01006711409 1
 
< 0.1%
ValueCountFrequency (%)
0.9865771812 1
 
< 0.1%
0.9090909091 3
< 0.1%
0.8823529412 1
 
< 0.1%
0.8275 1
 
< 0.1%
0.75 6
< 0.1%
0.6060606061 1
 
< 0.1%
0.5459508644 1
 
< 0.1%
0.5420054201 2
 
< 0.1%
0.5020080321 3
< 0.1%
0.5017301038 1
 
< 0.1%

pct_coupon_disc
Real number (ℝ)

Distinct152
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0073780361
Minimum-0
Maximum0.94850949
Zeros14386
Zeros (%)97.0%
Negative0
Negative (%)0.0%
Memory size116.0 KiB
2023-05-28T10:58:24.677411image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-0
5-th percentile0
Q10
median0
Q30
95-th percentile-0
Maximum0.94850949
Range0.94850949
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.050267021
Coefficient of variation (CV)6.8130625
Kurtosis94.956913
Mean0.0073780361
Median Absolute Deviation (MAD)0
Skewness8.8566648
Sum109.4089
Variance0.0025267734
MonotonicityNot monotonic
2023-05-28T10:58:24.813578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0 14386
97.0%
0.2227171492 18
 
0.1%
0.2386634845 18
 
0.1%
0.2865329513 16
 
0.1%
0.278551532 16
 
0.1%
0.0834028357 15
 
0.1%
0.139275766 14
 
0.1%
0.16 14
 
0.1%
0.3344481605 12
 
0.1%
0.2004008016 11
 
0.1%
Other values (142) 309
 
2.1%
ValueCountFrequency (%)
-0 14386
97.0%
0.005297233667 1
 
< 0.1%
0.02000666889 1
 
< 0.1%
0.03334444815 1
 
< 0.1%
0.03356831151 1
 
< 0.1%
0.04547521601 1
 
< 0.1%
0.04786979416 2
 
< 0.1%
0.05408328826 2
 
< 0.1%
0.05885815185 3
 
< 0.1%
0.0652173913 1
 
< 0.1%
ValueCountFrequency (%)
0.9485094851 1
 
< 0.1%
0.8474576271 1
 
< 0.1%
0.8403361345 4
< 0.1%
0.8016032064 1
 
< 0.1%
0.7194244604 1
 
< 0.1%
0.6787148594 1
 
< 0.1%
0.6666666667 1
 
< 0.1%
0.6435272045 1
 
< 0.1%
0.6329113924 1
 
< 0.1%
0.6329113924 1
 
< 0.1%

display_1
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14829
100.0%

Length

2023-05-28T10:58:24.929739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:25.028121image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14829
100.0%

Most occurring characters

ValueCountFrequency (%)
0 14829
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14829
100.0%

display_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14828 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Length

2023-05-28T10:58:25.108519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:25.201786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14828
> 99.9%
1 1
 
< 0.1%

display_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14804 
1
 
25

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Length

2023-05-28T10:58:25.281084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:25.373307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14804
99.8%
1 25
 
0.2%

display_4
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14827 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Length

2023-05-28T10:58:25.455179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:25.554478image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

display_5
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14765 
1
 
64

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Length

2023-05-28T10:58:25.636179image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:25.731323image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14765
99.6%
1 64
 
0.4%

display_6
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14827 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Length

2023-05-28T10:58:25.841656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:25.945505image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

display_7
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14825 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Length

2023-05-28T10:58:26.030835image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:26.134206image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

display_9
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14777 
1
 
52

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Length

2023-05-28T10:58:26.229474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:26.338233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14777
99.6%
1 52
 
0.4%

display_A
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14829 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14829
100.0%

Length

2023-05-28T10:58:26.428515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:26.523777image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14829
100.0%

Most occurring characters

ValueCountFrequency (%)
0 14829
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14829
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14829
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14829
100.0%

mailer_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14551 
1
 
278

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Length

2023-05-28T10:58:26.601088image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:26.698264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring characters

ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14551
98.1%
1 278
 
1.9%

mailer_C
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14827 
1
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Length

2023-05-28T10:58:26.780103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:26.878359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14827
> 99.9%
1 2
 
< 0.1%

mailer_F
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14825 
1
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Length

2023-05-28T10:58:26.960146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:27.053414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14825
> 99.9%
1 4
 
< 0.1%

mailer_H
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14758 
1
 
71

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Length

2023-05-28T10:58:27.137508image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:27.234431image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14758
99.5%
1 71
 
0.5%

mailer_L
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14824 
1
 
5

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Length

2023-05-28T10:58:27.320933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:27.426754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14824
> 99.9%
1 5
 
< 0.1%

marital_status_A
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
1
7420 
0
7409 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Length

2023-05-28T10:58:27.510802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:27.608696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring characters

ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 7420
50.0%
0 7409
50.0%

marital_status_B
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12921 
1
1908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Length

2023-05-28T10:58:27.695067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:27.796097image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring characters

ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12921
87.1%
1 1908
 
12.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
1
9444 
0
5385 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Length

2023-05-28T10:58:27.885518image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:27.985731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring characters

ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9444
63.7%
0 5385
36.3%

homeowner_Probable Owner
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14243 
1
 
586

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Length

2023-05-28T10:58:28.076035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:28.174456image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14243
96.0%
1 586
 
4.0%

homeowner_Probable Renter
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14514 
1
 
315

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Length

2023-05-28T10:58:28.255294image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:28.348691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14514
97.9%
1 315
 
2.1%

homeowner_Renter
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13566 
1
 
1263

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Length

2023-05-28T10:58:28.683848image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:28.811879image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring characters

ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13566
91.5%
1 1263
 
8.5%

hhcomp_1 Adult Kids
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13466 
1
1363 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Length

2023-05-28T10:58:28.911919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:29.047949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring characters

ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13466
90.8%
1 1363
 
9.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12188 
1
2641 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Length

2023-05-28T10:58:29.148092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:29.260235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring characters

ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12188
82.2%
1 2641
 
17.8%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
9309 
1
5520 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Length

2023-05-28T10:58:29.360308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:29.464523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring characters

ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9309
62.8%
1 5520
37.2%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
11852 
1
2977 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Length

2023-05-28T10:58:29.552534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:29.656650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring characters

ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11852
79.9%
1 2977
 
20.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14040 
1
 
789

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Length

2023-05-28T10:58:29.760730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:29.868861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14040
94.7%
1 789
 
5.3%

kid_category_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12423 
1
2406 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Length

2023-05-28T10:58:29.960944image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:30.064116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring characters

ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12423
83.8%
1 2406
 
16.2%

kid_category_2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13751 
1
 
1078

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Length

2023-05-28T10:58:30.161256image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:30.297457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13751
92.7%
1 1078
 
7.3%

kid_category_3+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14048 
1
 
781

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Length

2023-05-28T10:58:30.441494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:30.609735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14048
94.7%
1 781
 
5.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
1
10564 
0
4265 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Length

2023-05-28T10:58:30.741805image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:30.885889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring characters

ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10564
71.2%
0 4265
28.8%

age_19-24
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14110 
1
 
719

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14110
95.2%
1 719
 
4.8%

Length

2023-05-28T10:58:31.001934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:31.202019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14110
95.2%
1 719
 
4.8%

Most occurring characters

ValueCountFrequency (%)
0 14110
95.2%
1 719
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14110
95.2%
1 719
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14110
95.2%
1 719
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14110
95.2%
1 719
 
4.8%

age_25-34
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13090 
1
1739 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13090
88.3%
1 1739
 
11.7%

Length

2023-05-28T10:58:31.358068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:31.522136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13090
88.3%
1 1739
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 13090
88.3%
1 1739
 
11.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13090
88.3%
1 1739
 
11.7%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13090
88.3%
1 1739
 
11.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13090
88.3%
1 1739
 
11.7%

age_35-44
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
10368 
1
4461 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10368
69.9%
1 4461
30.1%

Length

2023-05-28T10:58:31.678218image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:31.834262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10368
69.9%
1 4461
30.1%

Most occurring characters

ValueCountFrequency (%)
0 10368
69.9%
1 4461
30.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10368
69.9%
1 4461
30.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10368
69.9%
1 4461
30.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10368
69.9%
1 4461
30.1%

age_45-54
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
8688 
1
6141 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 8688
58.6%
1 6141
41.4%

Length

2023-05-28T10:58:31.946299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:32.078435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8688
58.6%
1 6141
41.4%

Most occurring characters

ValueCountFrequency (%)
0 8688
58.6%
1 6141
41.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8688
58.6%
1 6141
41.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8688
58.6%
1 6141
41.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8688
58.6%
1 6141
41.4%

age_55-64
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13921 
1
 
908

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13921
93.9%
1 908
 
6.1%

Length

2023-05-28T10:58:32.196535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:32.337538image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13921
93.9%
1 908
 
6.1%

Most occurring characters

ValueCountFrequency (%)
0 13921
93.9%
1 908
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13921
93.9%
1 908
 
6.1%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13921
93.9%
1 908
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13921
93.9%
1 908
 
6.1%

age_65+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13968 
1
 
861

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13968
94.2%
1 861
 
5.8%

Length

2023-05-28T10:58:32.465969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:32.637561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13968
94.2%
1 861
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 13968
94.2%
1 861
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13968
94.2%
1 861
 
5.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13968
94.2%
1 861
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13968
94.2%
1 861
 
5.8%

income_100-124K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14677 
1
 
152

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14677
99.0%
1 152
 
1.0%

Length

2023-05-28T10:58:32.795969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:32.937926image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14677
99.0%
1 152
 
1.0%

Most occurring characters

ValueCountFrequency (%)
0 14677
99.0%
1 152
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14677
99.0%
1 152
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14677
99.0%
1 152
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14677
99.0%
1 152
 
1.0%

income_125-149K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14259 
1
 
570

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14259
96.2%
1 570
 
3.8%

Length

2023-05-28T10:58:33.079346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:33.251632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14259
96.2%
1 570
 
3.8%

Most occurring characters

ValueCountFrequency (%)
0 14259
96.2%
1 570
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14259
96.2%
1 570
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14259
96.2%
1 570
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14259
96.2%
1 570
 
3.8%

income_15-24K
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13956 
1
 
873

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13956
94.1%
1 873
 
5.9%

Length

2023-05-28T10:58:33.378440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:33.536570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13956
94.1%
1 873
 
5.9%

Most occurring characters

ValueCountFrequency (%)
0 13956
94.1%
1 873
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13956
94.1%
1 873
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13956
94.1%
1 873
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13956
94.1%
1 873
 
5.9%

income_150-174K
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13808 
1
 
1021

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13808
93.1%
1 1021
 
6.9%

Length

2023-05-28T10:58:33.696299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:33.839317image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13808
93.1%
1 1021
 
6.9%

Most occurring characters

ValueCountFrequency (%)
0 13808
93.1%
1 1021
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13808
93.1%
1 1021
 
6.9%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13808
93.1%
1 1021
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13808
93.1%
1 1021
 
6.9%

income_175-199K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14773 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Length

2023-05-28T10:58:34.009932image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:34.141973image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

income_200-249K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14748 
1
 
81

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14748
99.5%
1 81
 
0.5%

Length

2023-05-28T10:58:34.278135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:34.414174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14748
99.5%
1 81
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 14748
99.5%
1 81
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14748
99.5%
1 81
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14748
99.5%
1 81
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14748
99.5%
1 81
 
0.5%

income_25-34K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
13170 
1
1659 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13170
88.8%
1 1659
 
11.2%

Length

2023-05-28T10:58:34.538219image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:34.692134image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 13170
88.8%
1 1659
 
11.2%

Most occurring characters

ValueCountFrequency (%)
0 13170
88.8%
1 1659
 
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13170
88.8%
1 1659
 
11.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13170
88.8%
1 1659
 
11.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13170
88.8%
1 1659
 
11.2%

income_250K+
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14718 
1
 
111

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14718
99.3%
1 111
 
0.7%

Length

2023-05-28T10:58:34.824166image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:34.968725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14718
99.3%
1 111
 
0.7%

Most occurring characters

ValueCountFrequency (%)
0 14718
99.3%
1 111
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14718
99.3%
1 111
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14718
99.3%
1 111
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14718
99.3%
1 111
 
0.7%

income_35-49K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
11651 
1
3178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 11651
78.6%
1 3178
 
21.4%

Length

2023-05-28T10:58:35.088272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:35.228309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11651
78.6%
1 3178
 
21.4%

Most occurring characters

ValueCountFrequency (%)
0 11651
78.6%
1 3178
 
21.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11651
78.6%
1 3178
 
21.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11651
78.6%
1 3178
 
21.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11651
78.6%
1 3178
 
21.4%

income_50-74K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
11062 
1
3767 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 11062
74.6%
1 3767
 
25.4%

Length

2023-05-28T10:58:35.348384image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:35.516461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 11062
74.6%
1 3767
 
25.4%

Most occurring characters

ValueCountFrequency (%)
0 11062
74.6%
1 3767
 
25.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11062
74.6%
1 3767
 
25.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11062
74.6%
1 3767
 
25.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11062
74.6%
1 3767
 
25.4%

income_75-99K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12280 
1
2549 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12280
82.8%
1 2549
 
17.2%

Length

2023-05-28T10:58:35.680521image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:36.164696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12280
82.8%
1 2549
 
17.2%

Most occurring characters

ValueCountFrequency (%)
0 12280
82.8%
1 2549
 
17.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12280
82.8%
1 2549
 
17.2%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12280
82.8%
1 2549
 
17.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12280
82.8%
1 2549
 
17.2%

income_Under 15K
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14017 
1
 
812

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14017
94.5%
1 812
 
5.5%

Length

2023-05-28T10:58:36.304776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:36.488842image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14017
94.5%
1 812
 
5.5%

Most occurring characters

ValueCountFrequency (%)
0 14017
94.5%
1 812
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14017
94.5%
1 812
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14017
94.5%
1 812
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14017
94.5%
1 812
 
5.5%

hhsize_1
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
10614 
1
4215 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10614
71.6%
1 4215
 
28.4%

Length

2023-05-28T10:58:36.652885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:36.796937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 10614
71.6%
1 4215
 
28.4%

Most occurring characters

ValueCountFrequency (%)
0 10614
71.6%
1 4215
 
28.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10614
71.6%
1 4215
 
28.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10614
71.6%
1 4215
 
28.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10614
71.6%
1 4215
 
28.4%

hhsize_2
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
8191 
1
6638 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 8191
55.2%
1 6638
44.8%

Length

2023-05-28T10:58:36.901124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:37.021149image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 8191
55.2%
1 6638
44.8%

Most occurring characters

ValueCountFrequency (%)
0 8191
55.2%
1 6638
44.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8191
55.2%
1 6638
44.8%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8191
55.2%
1 6638
44.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8191
55.2%
1 6638
44.8%

hhsize_3
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
12251 
1
2578 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 12251
82.6%
1 2578
 
17.4%

Length

2023-05-28T10:58:37.117267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:37.233313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 12251
82.6%
1 2578
 
17.4%

Most occurring characters

ValueCountFrequency (%)
0 12251
82.6%
1 2578
 
17.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12251
82.6%
1 2578
 
17.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12251
82.6%
1 2578
 
17.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12251
82.6%
1 2578
 
17.4%

hhsize_4
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14178 
1
 
651

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14178
95.6%
1 651
 
4.4%

Length

2023-05-28T10:58:37.477375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:37.609522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14178
95.6%
1 651
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 14178
95.6%
1 651
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14178
95.6%
1 651
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14178
95.6%
1 651
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14178
95.6%
1 651
 
4.4%

hhsize_5+
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14082 
1
 
747

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14082
95.0%
1 747
 
5.0%

Length

2023-05-28T10:58:37.705766image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:37.805970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14082
95.0%
1 747
 
5.0%

Most occurring characters

ValueCountFrequency (%)
0 14082
95.0%
1 747
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14082
95.0%
1 747
 
5.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14082
95.0%
1 747
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14082
95.0%
1 747
 
5.0%

campaign_8.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14740 
1
 
89

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Length

2023-05-28T10:58:37.893994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:37.998151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14740
99.4%
1 89
 
0.6%

campaign_13.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14482 
1
 
347

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Length

2023-05-28T10:58:38.082174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:38.178409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14482
97.7%
1 347
 
2.3%

campaign_15.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14629 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Length

2023-05-28T10:58:38.258366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:38.354576image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

campaign_18.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14735 
1
 
94

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Length

2023-05-28T10:58:38.434750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:38.530985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring characters

ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14735
99.4%
1 94
 
0.6%

campaign_24.0
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14315 
1
 
514

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Length

2023-05-28T10:58:38.623151image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:38.735362image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

campaign_26.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14774 
1
 
55

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Length

2023-05-28T10:58:38.827404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:38.939430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14774
99.6%
1 55
 
0.4%

campaign_30.0
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14773 
1
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Length

2023-05-28T10:58:39.031549image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:39.131787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14773
99.6%
1 56
 
0.4%

description_TypeA
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14188 
1
 
641

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Length

2023-05-28T10:58:39.215889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:39.312009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring characters

ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14188
95.7%
1 641
 
4.3%

description_TypeB
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14315 
1
 
514

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Length

2023-05-28T10:58:39.392126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:39.500408image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring characters

ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14315
96.5%
1 514
 
3.5%

description_TypeC
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size116.0 KiB
0
14629 
1
 
200

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14829
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Length

2023-05-28T10:58:39.588562image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-05-28T10:58:39.696654image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14829
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 14829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14629
98.7%
1 200
 
1.3%

Interactions

2023-05-28T10:58:20.339126image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:16.777937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:17.640171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:18.394953image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:19.441694image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:20.490241image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:16.941369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:17.779452image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:18.551574image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:19.625885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:20.643442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:17.141587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:17.936509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:18.916716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:19.784062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:20.767993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:17.316287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:18.086955image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:19.102213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:19.973928image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:20.882801image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:17.466426image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:18.233392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:19.271432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-05-28T10:58:20.169072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-05-28T10:58:39.888739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Unnamed: 0shelf_pricepct_discpct_retail_discpct_coupon_discfirst_purchasedisplay_2display_3display_4display_5display_6display_7display_9mailer_Amailer_Cmailer_Fmailer_Hmailer_Lmarital_status_Amarital_status_Bhomeowner_Homeownerhomeowner_Probable Ownerhomeowner_Probable Renterhomeowner_Renterhhcomp_1 Adult Kidshhcomp_2 Adults Kidshhcomp_2 Adults No Kidshhcomp_Single Femalehhcomp_Single Malekid_category_1kid_category_2kid_category_3+kid_category_None/Unknownage_19-24age_25-34age_35-44age_45-54age_55-64age_65+income_100-124Kincome_125-149Kincome_15-24Kincome_150-174Kincome_175-199Kincome_200-249Kincome_25-34Kincome_250K+income_35-49Kincome_50-74Kincome_75-99Kincome_Under 15Khhsize_1hhsize_2hhsize_3hhsize_4hhsize_5+campaign_8.0campaign_13.0campaign_15.0campaign_18.0campaign_24.0campaign_26.0campaign_30.0description_TypeAdescription_TypeBdescription_TypeC
Unnamed: 01.0000.090-0.047-0.0500.0070.1540.0040.0000.0260.0130.0000.0000.1100.0290.0000.0090.0420.0220.3000.2070.3290.5280.2990.3120.3470.2980.3420.2750.1670.2940.3150.1750.2950.2310.3210.2820.2580.4830.2190.1390.2370.2510.4420.0870.1860.2470.1610.2770.1800.2470.1660.2960.2960.3010.2880.1710.0240.0870.1680.0490.0790.0790.0630.0960.0790.168
shelf_price0.0901.0000.1420.1160.1680.1850.0810.1210.0120.1280.0610.0300.2650.1040.2410.0540.0670.0950.0750.0320.1120.1030.0520.0870.1230.1230.0680.0700.0500.0390.0210.1220.0460.0460.0800.0630.0580.0730.0420.0720.0460.0520.0260.0290.1280.0710.1290.0420.0440.0820.0590.0460.0490.0690.0550.1290.0830.0830.0640.1140.0280.0300.0290.0930.0280.064
pct_disc-0.0470.1421.0000.9730.2330.0910.0000.0980.0000.0770.0000.0000.0480.2360.1880.0400.0640.0000.1060.0300.1500.1970.1040.1650.1620.1010.0870.0920.0390.0720.0110.0560.0420.0540.0320.1190.0260.1400.0640.0680.0320.0690.1130.0260.0070.0510.0710.0450.0880.0660.0200.0660.0510.0870.0530.0640.0400.1860.0990.0960.0630.0550.0550.1160.0630.099
pct_retail_disc-0.0500.1160.9731.0000.0400.0760.0000.0660.0000.0780.0000.0000.0470.2500.0230.0410.0640.0000.1030.0470.1480.2000.1010.1640.1630.0930.0870.0930.0780.0710.0190.0490.0410.0540.0280.1230.0330.1490.0890.0600.0240.0740.1220.0110.0040.0460.0710.0520.0930.0690.0130.0690.0500.0860.0550.0580.0330.1950.0980.0960.0460.0530.1120.1200.0460.098
pct_coupon_disc0.0070.1680.2330.0401.0000.0840.0000.0450.0000.0150.0000.0000.0000.0230.1260.0000.0000.0000.0430.0120.0590.0190.0410.0360.0380.0590.0440.0300.0020.0090.0270.0350.0280.0190.0170.0060.0000.0930.0150.0790.0000.0230.0710.0000.0000.0370.0000.0000.0270.0380.0130.0250.0450.0130.0460.0370.0220.0000.0000.0280.1030.0000.0000.0000.1030.000
first_purchase0.1540.1850.0910.0760.0841.0000.0000.0100.0070.0240.0000.0000.0230.0050.0000.0000.0000.0000.0760.0170.0180.0480.0050.0880.0650.0800.0330.0310.0530.0120.0000.0840.0260.0040.0510.0320.0170.0500.0000.0540.0630.0700.0640.0270.0000.0230.0100.0190.0320.0350.0260.0470.0720.0310.0470.0760.0000.0460.0240.0040.0560.0170.0170.0420.0560.024
display_20.0040.0810.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0540.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_30.0000.1210.0980.0660.0450.0100.0001.0000.0000.0000.0000.0000.0000.0730.0000.0000.0040.0430.0000.0000.0000.0000.0000.0050.0060.0000.0000.0060.0290.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0120.0000.0000.0000.0120.000
display_40.0260.0120.0000.0000.0000.0070.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0260.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_50.0130.1280.0770.0780.0150.0240.0000.0000.0001.0000.0000.0000.0000.0780.0000.0000.0000.0260.0000.0000.0240.0070.0000.0160.0020.0230.0000.0030.0000.0000.0000.0320.0110.0000.0050.0000.0000.0000.0000.0000.0000.0000.0080.0000.0000.0090.0000.0000.0000.0000.0000.0100.0000.0000.0000.0330.0120.0000.0000.0000.0000.0000.0000.0000.0000.000
display_60.0000.0610.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_70.0000.0300.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0100.0000.0000.0000.0000.0000.0000.0200.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
display_90.1100.2650.0480.0470.0000.0230.0000.0000.0000.0000.0000.0001.0000.0280.0000.0000.0000.0000.0410.0000.0480.0000.0000.0140.0000.0220.0540.0240.0000.0130.0120.0000.0200.0000.0100.0320.0460.0000.0000.0000.0030.0000.0110.0000.0000.0170.0000.0170.0680.0140.0000.0270.0460.0180.0060.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.000
mailer_A0.0290.1040.2360.2500.0230.0050.0000.0730.0000.0780.0000.0000.0281.0000.0000.0000.0000.0000.0220.0000.0030.0110.0000.0130.0210.0000.0130.0140.0000.0000.0060.0000.0000.0000.0000.0190.0080.0180.0000.0000.0070.0090.0190.0000.0000.0000.0000.0050.0000.0030.0000.0310.0200.0050.0000.0000.0000.0180.0110.0000.0000.0000.0000.0070.0000.011
mailer_C0.0000.2410.1880.0230.1260.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_F0.0090.0540.0400.0410.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0090.0000.0000.0000.0000.0000.0000.0000.0030.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0180.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
mailer_H0.0420.0670.0640.0640.0000.0000.0000.0040.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.0180.0000.0310.0020.0400.0000.0060.0090.0310.0290.0120.0070.0000.0000.0130.0000.0000.0030.0000.0130.0130.0000.0040.0200.0000.0000.0000.0190.0000.0000.0000.0210.0120.0160.0250.0000.0080.0030.0000.0000.0000.0000.0000.0000.0000.0090.0000.000
mailer_L0.0220.0950.0000.0000.0000.0000.0000.0430.0000.0260.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0060.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.000
marital_status_A0.3000.0750.1060.1030.0430.0760.0000.0000.0000.0000.0000.0000.0410.0220.0000.0090.0180.0001.0000.3840.4200.1830.1470.0540.0540.2390.2860.2410.2080.2480.1160.1290.1990.0260.1280.0420.0050.1140.0190.0670.1220.1710.1420.0330.0730.1790.0000.0630.0240.1380.0440.6300.3510.2110.0000.1430.0190.0730.0400.0210.0180.0480.0330.0290.0180.040
marital_status_B0.2070.0320.0300.0470.0120.0170.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3841.0000.0820.0710.0550.3950.3000.1110.2350.1220.2610.0450.1730.0390.0810.0830.1990.0860.1550.0350.1060.0210.0200.0360.0220.0070.0260.2230.0310.0680.1450.0620.0610.2170.2050.0260.0460.0100.0490.0270.1020.0000.0270.0340.0630.0270.0270.102
homeowner_Homeowner0.3290.1120.1500.1480.0590.0180.0000.0000.0040.0240.0000.0040.0480.0030.0000.0000.0310.0000.4200.0821.0000.2680.1940.4040.3250.1650.3710.2390.0000.0330.1190.0580.0660.0730.1000.1850.1070.1600.1790.0000.1090.1960.2020.0340.0350.1000.0480.1600.0600.3170.1160.2900.2720.0420.0190.0730.0280.0150.0190.0410.0000.0250.0000.0160.0000.019
homeowner_Probable Owner0.5280.1030.1970.2000.0190.0480.0000.0000.0000.0070.0000.0000.0000.0110.0000.0000.0020.0000.1830.0710.2681.0000.0270.0610.4800.0850.0920.0720.0470.3470.0560.0460.2260.0440.0480.2830.1630.0500.0470.0090.0390.0490.0470.0050.0100.0650.0130.2930.0430.0920.0470.1190.1050.3310.0420.0450.0110.0910.0000.0110.0370.0050.0050.0470.0370.000
homeowner_Probable Renter0.2990.0520.1040.1010.0410.0050.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0400.0000.1470.0550.1940.0271.0000.0430.0450.0670.1130.1610.0290.0640.0400.0330.0930.0060.0520.0940.0120.0320.0350.0100.0270.5140.0380.0000.0000.0450.0060.0520.0850.0660.0050.2330.1320.0660.0290.0320.0000.0200.0130.0030.0110.0000.0000.0220.0110.013
homeowner_Renter0.3120.0870.1650.1640.0360.0880.0000.0050.0260.0160.0000.0000.0140.0130.0000.0000.0000.0000.0540.3950.4040.0610.0431.0000.3820.0420.2030.0670.0650.0690.2380.0870.2360.0880.1770.0910.1710.0660.0750.0290.0470.0730.0820.0150.0190.2710.0240.0750.0160.1070.1490.1670.0200.1390.0000.0540.0620.0220.0340.0110.0340.0030.0000.0320.0340.034
hhcomp_1 Adult Kids0.3470.1230.1620.1630.0380.0650.0000.0060.0000.0020.0000.0000.0000.0210.0000.0000.0060.0000.0540.3000.3250.4800.0450.3821.0000.1480.2450.1590.0740.3670.3370.0130.5000.0330.2300.1090.1890.0800.0130.0300.0600.0000.0860.0160.0200.2440.0250.1610.1620.1360.0890.2000.1500.4540.0110.0140.0000.0540.0200.0220.0240.0150.0000.0250.0240.020
hhcomp_2 Adults Kids0.2980.1230.1010.0930.0590.0800.0000.0000.0000.0230.0000.0000.0220.0000.0000.0000.0090.0000.2390.1110.1650.0850.0670.0420.1481.0000.3580.2330.1100.4490.2790.4180.7320.2290.0520.0100.0590.0730.1080.1090.2940.0740.0490.0670.0000.1260.0320.0250.0140.0000.0000.2930.4190.4220.4190.4310.0230.0230.0000.0000.0030.0090.0000.0150.0030.000
hhcomp_2 Adults No Kids0.3420.0680.0870.0870.0440.0330.0000.0000.0000.0000.0000.0000.0540.0130.0000.0030.0310.0000.2860.2350.3710.0920.1130.2030.2450.3581.0000.3860.1820.3390.2150.1810.4890.0700.1130.1010.0900.1380.0860.0390.1160.0290.2330.0290.0660.1490.0910.0550.0640.2260.1010.4850.8550.3530.1640.1770.0000.0090.0600.0420.0220.0380.0390.0000.0220.060
hhcomp_Single Female0.2750.0700.0920.0930.0300.0310.0000.0060.0000.0030.0000.0000.0240.0140.0000.0000.0290.0000.2410.1220.2390.0720.1610.0670.1590.2330.3861.0000.1180.2200.1400.1180.3180.0570.0690.1620.0530.0110.0410.0000.0500.1060.0970.0000.0350.2020.0220.0630.0310.1400.0680.5070.1890.2300.1070.1150.0000.0030.1210.0180.0250.0280.0060.0150.0250.121
hhcomp_Single Male0.1670.0500.0390.0780.0020.0530.0000.0290.0000.0000.0060.0000.0000.0000.0000.0000.0120.0000.2080.2610.0000.0470.0290.0650.0740.1100.1820.1181.0000.1040.0650.0550.1500.0030.1260.0780.1320.0380.2090.0070.0460.0000.0000.0000.0130.0160.0170.2060.1140.0420.0380.3440.1840.1080.0490.0530.0030.0240.0000.0050.0120.0720.0900.0200.0120.000
kid_category_10.2940.0390.0720.0710.0090.0120.0000.0000.0000.0000.0000.0000.0130.0000.0000.0000.0070.0000.2480.0450.0330.3470.0640.0690.3670.4490.3390.2200.1041.0000.1230.1030.6920.1570.0510.1840.1110.0950.1020.0000.2270.0240.0890.0570.0000.0710.0300.0690.0810.0220.0220.2770.2900.8200.0930.1010.0000.0470.0100.0160.0190.0170.0000.0230.0190.010
kid_category_20.3150.0210.0110.0190.0270.0000.0000.0000.0000.0000.0000.0000.0120.0060.0000.0000.0000.0000.1160.1730.1190.0560.0400.2380.3370.2790.2150.1400.0650.1231.0000.0650.4400.0550.2920.1350.0210.0360.0000.0520.0000.0420.0630.0130.0170.2060.0210.0000.0250.0900.0400.1760.2520.1870.7220.0630.0110.0290.0310.0000.0320.0120.0130.0390.0320.031
kid_category_3+0.1750.1220.0560.0490.0350.0840.0000.0000.0000.0320.0000.0000.0000.0000.0000.0000.0000.0000.1290.0390.0580.0460.0330.0870.0130.4180.1810.1180.0550.1030.0651.0000.3710.1340.1160.0150.0990.0490.0570.0820.0900.0490.0000.0000.0130.0490.0170.0180.0530.0250.1230.1480.2120.1070.0000.9760.0490.0220.0250.0000.0350.0000.0000.0000.0350.025
kid_category_None/Unknown0.2950.0460.0420.0410.0280.0260.0000.0000.0000.0110.0000.0000.0200.0000.0000.0000.0130.0000.1990.0810.0660.2260.0930.2360.5000.7320.4890.3180.1500.6920.4400.3711.0000.1630.1820.0810.1520.1240.1110.0670.2310.0700.1070.0410.0130.0340.0480.0610.1080.0450.1030.4000.4850.7220.3370.3620.0190.0020.0190.0180.0130.0240.0030.0000.0130.019
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Missing values

2023-05-28T10:58:21.200415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-05-28T10:58:22.068004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

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328True2.490.000000-0.000000-0.00000000000000000000101000001000001000100000000000100010000000000000
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530True2.590.000000-0.000000-0.00000000000000000000101000001000001000100000000000100010000000000000
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732False2.590.1930500.193050-0.00000000000000000000101000001000001000100000000000100010000000000000
833False2.590.000000-0.000000-0.00000000000000000000101000001000001000100000000000100010000000000000
934True12.490.2802240.2001600.08006400000000000000000000010001000010000000000100000001000000000000
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1482522223True1.990.0502510.050251-0.000000000000000000000010001000010000000000000001001000000000000
1482622224True2.990.1337790.133779-0.000000000000000000000010001000010000000000000001001000000000000
1482722225False3.190.1880880.188088-0.000000000000000000000010001000010000000000000001001000000000000
1482822226True2.990.0668900.066890-0.000000000000000000000010001000010000000000000001001000000000000